Overview

Dataset statistics

Number of variables24
Number of observations4000
Missing cells18
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory645.1 KiB
Average record size in memory165.2 B

Variable types

Numeric13
Categorical11

Alerts

Departure Delay is highly overall correlated with Arrival DelayHigh correlation
Arrival Delay is highly overall correlated with Departure DelayHigh correlation
Ease of Online Booking is highly overall correlated with In-flight Wifi ServiceHigh correlation
Online Boarding is highly overall correlated with SatisfactionHigh correlation
Cleanliness is highly overall correlated with Food and Drink and 2 other fieldsHigh correlation
Food and Drink is highly overall correlated with Cleanliness and 2 other fieldsHigh correlation
In-flight Wifi Service is highly overall correlated with Ease of Online Booking and 1 other fieldsHigh correlation
In-flight Entertainment is highly overall correlated with Cleanliness and 2 other fieldsHigh correlation
Type of Travel is highly overall correlated with ClassHigh correlation
Class is highly overall correlated with Type of Travel and 1 other fieldsHigh correlation
Seat Comfort is highly overall correlated with Cleanliness and 2 other fieldsHigh correlation
Satisfaction is highly overall correlated with Online Boarding and 2 other fieldsHigh correlation
ID has unique valuesUnique
Departure Delay has 2245 (56.1%) zerosZeros
Arrival Delay has 2237 (55.9%) zerosZeros
Departure and Arrival Time Convenience has 227 (5.7%) zerosZeros
Ease of Online Booking has 172 (4.3%) zerosZeros
Online Boarding has 100 (2.5%) zerosZeros
In-flight Wifi Service has 121 (3.0%) zerosZeros

Reproduction

Analysis started2023-02-21 17:48:47.699862
Analysis finished2023-02-21 17:49:06.350416
Duration18.65 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Distinct4000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65001.709
Minimum88
Maximum129877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-02-21T19:49:06.444931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum88
5-th percentile5668.95
Q131660.5
median64923
Q398662
95-th percentile123446.8
Maximum129877
Range129789
Interquartile range (IQR)67001.5

Descriptive statistics

Standard deviation37945.495
Coefficient of variation (CV)0.58376149
Kurtosis-1.2239567
Mean65001.709
Median Absolute Deviation (MAD)33526
Skewness-0.0025877839
Sum2.6000684 × 108
Variance1.4398606 × 109
MonotonicityNot monotonic
2023-02-21T19:49:06.550542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55782 1
 
< 0.1%
110260 1
 
< 0.1%
46287 1
 
< 0.1%
53161 1
 
< 0.1%
31700 1
 
< 0.1%
106243 1
 
< 0.1%
126695 1
 
< 0.1%
53546 1
 
< 0.1%
82552 1
 
< 0.1%
23379 1
 
< 0.1%
Other values (3990) 3990
99.8%
ValueCountFrequency (%)
88 1
< 0.1%
136 1
< 0.1%
156 1
< 0.1%
191 1
< 0.1%
196 1
< 0.1%
238 1
< 0.1%
241 1
< 0.1%
264 1
< 0.1%
341 1
< 0.1%
359 1
< 0.1%
ValueCountFrequency (%)
129877 1
< 0.1%
129872 1
< 0.1%
129860 1
< 0.1%
129826 1
< 0.1%
129807 1
< 0.1%
129802 1
< 0.1%
129792 1
< 0.1%
129783 1
< 0.1%
129778 1
< 0.1%
129763 1
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.3 KiB
Female
2015 
Male
1985 

Length

Max length6
Median length6
Mean length5.0075
Min length4

Characters and Unicode

Total characters20030
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 2015
50.4%
Male 1985
49.6%

Length

2023-02-21T19:49:06.647695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T19:49:06.742649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
female 2015
50.4%
male 1985
49.6%

Most occurring characters

ValueCountFrequency (%)
e 6015
30.0%
a 4000
20.0%
l 4000
20.0%
F 2015
 
10.1%
m 2015
 
10.1%
M 1985
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16030
80.0%
Uppercase Letter 4000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6015
37.5%
a 4000
25.0%
l 4000
25.0%
m 2015
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F 2015
50.4%
M 1985
49.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 20030
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6015
30.0%
a 4000
20.0%
l 4000
20.0%
F 2015
 
10.1%
m 2015
 
10.1%
M 1985
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20030
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6015
30.0%
a 4000
20.0%
l 4000
20.0%
F 2015
 
10.1%
m 2015
 
10.1%
M 1985
 
9.9%

Age
Real number (ℝ)

Distinct73
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.374
Minimum7
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-02-21T19:49:06.827038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q127
median40
Q351
95-th percentile64
Maximum80
Range73
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.234357
Coefficient of variation (CV)0.38691414
Kurtosis-0.71941308
Mean39.374
Median Absolute Deviation (MAD)12
Skewness-0.00062589655
Sum157496
Variance232.08565
MonotonicityNot monotonic
2023-02-21T19:49:06.923283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 110
 
2.8%
39 109
 
2.7%
24 104
 
2.6%
45 102
 
2.5%
41 98
 
2.5%
37 97
 
2.4%
44 94
 
2.4%
49 93
 
2.3%
48 92
 
2.3%
47 90
 
2.2%
Other values (63) 3011
75.3%
ValueCountFrequency (%)
7 24
0.6%
8 33
0.8%
9 22
0.5%
10 20
0.5%
11 29
0.7%
12 27
0.7%
13 30
0.8%
14 27
0.7%
15 28
0.7%
16 34
0.9%
ValueCountFrequency (%)
80 2
 
0.1%
79 3
 
0.1%
78 2
 
0.1%
77 5
 
0.1%
76 2
 
0.1%
74 3
 
0.1%
73 3
 
0.1%
72 10
 
0.2%
71 4
 
0.1%
70 26
0.7%

Customer Type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.3 KiB
Returning
3259 
First-time
741 

Length

Max length10
Median length9
Mean length9.18525
Min length9

Characters and Unicode

Total characters36741
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReturning
2nd rowReturning
3rd rowFirst-time
4th rowReturning
5th rowReturning

Common Values

ValueCountFrequency (%)
Returning 3259
81.5%
First-time 741
 
18.5%

Length

2023-02-21T19:49:07.015891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T19:49:07.099216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
returning 3259
81.5%
first-time 741
 
18.5%

Most occurring characters

ValueCountFrequency (%)
n 6518
17.7%
t 4741
12.9%
i 4741
12.9%
e 4000
10.9%
r 4000
10.9%
R 3259
8.9%
u 3259
8.9%
g 3259
8.9%
F 741
 
2.0%
s 741
 
2.0%
Other values (2) 1482
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32000
87.1%
Uppercase Letter 4000
 
10.9%
Dash Punctuation 741
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 6518
20.4%
t 4741
14.8%
i 4741
14.8%
e 4000
12.5%
r 4000
12.5%
u 3259
10.2%
g 3259
10.2%
s 741
 
2.3%
m 741
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
R 3259
81.5%
F 741
 
18.5%
Dash Punctuation
ValueCountFrequency (%)
- 741
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36000
98.0%
Common 741
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 6518
18.1%
t 4741
13.2%
i 4741
13.2%
e 4000
11.1%
r 4000
11.1%
R 3259
9.1%
u 3259
9.1%
g 3259
9.1%
F 741
 
2.1%
s 741
 
2.1%
Common
ValueCountFrequency (%)
- 741
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 6518
17.7%
t 4741
12.9%
i 4741
12.9%
e 4000
10.9%
r 4000
10.9%
R 3259
8.9%
u 3259
8.9%
g 3259
8.9%
F 741
 
2.0%
s 741
 
2.0%
Other values (2) 1482
 
4.0%

Type of Travel
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.3 KiB
Business
2742 
Personal
1258 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters32000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal
2nd rowPersonal
3rd rowBusiness
4th rowPersonal
5th rowBusiness

Common Values

ValueCountFrequency (%)
Business 2742
68.5%
Personal 1258
31.4%

Length

2023-02-21T19:49:07.164741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T19:49:07.242301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
business 2742
68.5%
personal 1258
31.4%

Most occurring characters

ValueCountFrequency (%)
s 9484
29.6%
n 4000
12.5%
e 4000
12.5%
B 2742
 
8.6%
u 2742
 
8.6%
i 2742
 
8.6%
P 1258
 
3.9%
r 1258
 
3.9%
o 1258
 
3.9%
a 1258
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28000
87.5%
Uppercase Letter 4000
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 9484
33.9%
n 4000
14.3%
e 4000
14.3%
u 2742
 
9.8%
i 2742
 
9.8%
r 1258
 
4.5%
o 1258
 
4.5%
a 1258
 
4.5%
l 1258
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
B 2742
68.5%
P 1258
31.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 32000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 9484
29.6%
n 4000
12.5%
e 4000
12.5%
B 2742
 
8.6%
u 2742
 
8.6%
i 2742
 
8.6%
P 1258
 
3.9%
r 1258
 
3.9%
o 1258
 
3.9%
a 1258
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 9484
29.6%
n 4000
12.5%
e 4000
12.5%
B 2742
 
8.6%
u 2742
 
8.6%
i 2742
 
8.6%
P 1258
 
3.9%
r 1258
 
3.9%
o 1258
 
3.9%
a 1258
 
3.9%

Class
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.3 KiB
Business
1891 
Economy
1835 
Economy Plus
274 

Length

Max length12
Median length8
Mean length7.81525
Min length7

Characters and Unicode

Total characters31261
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEconomy
2nd rowEconomy
3rd rowEconomy
4th rowEconomy
5th rowBusiness

Common Values

ValueCountFrequency (%)
Business 1891
47.3%
Economy 1835
45.9%
Economy Plus 274
 
6.9%

Length

2023-02-21T19:49:07.325623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T19:49:07.426477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
economy 2109
49.3%
business 1891
44.2%
plus 274
 
6.4%

Most occurring characters

ValueCountFrequency (%)
s 5947
19.0%
o 4218
13.5%
n 4000
12.8%
u 2165
 
6.9%
E 2109
 
6.7%
c 2109
 
6.7%
m 2109
 
6.7%
y 2109
 
6.7%
B 1891
 
6.0%
i 1891
 
6.0%
Other values (4) 2713
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26713
85.5%
Uppercase Letter 4274
 
13.7%
Space Separator 274
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 5947
22.3%
o 4218
15.8%
n 4000
15.0%
u 2165
 
8.1%
c 2109
 
7.9%
m 2109
 
7.9%
y 2109
 
7.9%
i 1891
 
7.1%
e 1891
 
7.1%
l 274
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
E 2109
49.3%
B 1891
44.2%
P 274
 
6.4%
Space Separator
ValueCountFrequency (%)
274
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 30987
99.1%
Common 274
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 5947
19.2%
o 4218
13.6%
n 4000
12.9%
u 2165
 
7.0%
E 2109
 
6.8%
c 2109
 
6.8%
m 2109
 
6.8%
y 2109
 
6.8%
B 1891
 
6.1%
i 1891
 
6.1%
Other values (3) 2439
7.9%
Common
ValueCountFrequency (%)
274
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 5947
19.0%
o 4218
13.5%
n 4000
12.8%
u 2165
 
6.9%
E 2109
 
6.7%
c 2109
 
6.7%
m 2109
 
6.7%
y 2109
 
6.7%
B 1891
 
6.0%
i 1891
 
6.0%
Other values (4) 2713
8.7%

Flight Distance
Real number (ℝ)

Distinct1533
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1173.3343
Minimum67
Maximum4817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-02-21T19:49:07.540969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile173
Q1403
median818.5
Q31727
95-th percentile3379.05
Maximum4817
Range4750
Interquartile range (IQR)1324

Descriptive statistics

Standard deviation995.26161
Coefficient of variation (CV)0.84823367
Kurtosis0.27229581
Mean1173.3343
Median Absolute Deviation (MAD)513.5
Skewness1.1108913
Sum4693337
Variance990545.68
MonotonicityNot monotonic
2023-02-21T19:49:07.646538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 26
 
0.7%
404 23
 
0.6%
296 20
 
0.5%
308 19
 
0.5%
447 17
 
0.4%
862 17
 
0.4%
214 15
 
0.4%
679 15
 
0.4%
2475 15
 
0.4%
594 15
 
0.4%
Other values (1523) 3818
95.5%
ValueCountFrequency (%)
67 6
0.1%
73 1
 
< 0.1%
74 1
 
< 0.1%
78 1
 
< 0.1%
82 1
 
< 0.1%
83 2
 
0.1%
86 5
0.1%
89 11
0.3%
96 3
 
0.1%
100 1
 
< 0.1%
ValueCountFrequency (%)
4817 1
< 0.1%
4000 1
< 0.1%
3998 1
< 0.1%
3995 1
< 0.1%
3993 1
< 0.1%
3987 1
< 0.1%
3985 1
< 0.1%
3983 1
< 0.1%
3980 2
0.1%
3979 1
< 0.1%

Departure Delay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct194
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.8965
Minimum0
Maximum595
Zeros2245
Zeros (%)56.1%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-02-21T19:49:07.750087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile77
Maximum595
Range595
Interquartile range (IQR)12

Descriptive statistics

Standard deviation37.276355
Coefficient of variation (CV)2.5023566
Kurtosis37.088732
Mean14.8965
Median Absolute Deviation (MAD)0
Skewness5.0287234
Sum59586
Variance1389.5267
MonotonicityNot monotonic
2023-02-21T19:49:07.844684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2245
56.1%
1 116
 
2.9%
2 86
 
2.1%
4 79
 
2.0%
3 69
 
1.7%
5 66
 
1.7%
10 57
 
1.4%
7 54
 
1.4%
6 53
 
1.3%
8 50
 
1.2%
Other values (184) 1125
28.1%
ValueCountFrequency (%)
0 2245
56.1%
1 116
 
2.9%
2 86
 
2.1%
3 69
 
1.7%
4 79
 
2.0%
5 66
 
1.7%
6 53
 
1.3%
7 54
 
1.4%
8 50
 
1.2%
9 42
 
1.1%
ValueCountFrequency (%)
595 1
< 0.1%
352 1
< 0.1%
351 1
< 0.1%
332 1
< 0.1%
328 1
< 0.1%
315 1
< 0.1%
311 1
< 0.1%
306 1
< 0.1%
303 1
< 0.1%
298 1
< 0.1%

Arrival Delay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct194
Distinct (%)4.9%
Missing18
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean15.293069
Minimum0
Maximum589
Zeros2237
Zeros (%)55.9%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-02-21T19:49:08.140769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile81.95
Maximum589
Range589
Interquartile range (IQR)14

Descriptive statistics

Standard deviation37.794893
Coefficient of variation (CV)2.471374
Kurtosis35.416465
Mean15.293069
Median Absolute Deviation (MAD)0
Skewness4.9087757
Sum60897
Variance1428.4539
MonotonicityNot monotonic
2023-02-21T19:49:08.479671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2237
55.9%
1 87
 
2.2%
2 85
 
2.1%
4 76
 
1.9%
3 73
 
1.8%
6 59
 
1.5%
5 57
 
1.4%
11 55
 
1.4%
8 54
 
1.4%
7 51
 
1.3%
Other values (184) 1148
28.7%
ValueCountFrequency (%)
0 2237
55.9%
1 87
 
2.2%
2 85
 
2.1%
3 73
 
1.8%
4 76
 
1.9%
5 57
 
1.4%
6 59
 
1.5%
7 51
 
1.3%
8 54
 
1.4%
9 39
 
1.0%
ValueCountFrequency (%)
589 1
< 0.1%
404 1
< 0.1%
350 1
< 0.1%
346 1
< 0.1%
336 1
< 0.1%
324 1
< 0.1%
321 1
< 0.1%
313 1
< 0.1%
299 1
< 0.1%
287 1
< 0.1%
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0495
Minimum0
Maximum5
Zeros227
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-02-21T19:49:08.568213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5413123
Coefficient of variation (CV)0.50543116
Kurtosis-1.042576
Mean3.0495
Median Absolute Deviation (MAD)1
Skewness-0.34076601
Sum12198
Variance2.3756437
MonotonicityNot monotonic
2023-02-21T19:49:08.632789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 984
24.6%
5 867
21.7%
3 671
16.8%
2 663
16.6%
1 588
14.7%
0 227
 
5.7%
ValueCountFrequency (%)
0 227
 
5.7%
1 588
14.7%
2 663
16.6%
3 671
16.8%
4 984
24.6%
5 867
21.7%
ValueCountFrequency (%)
5 867
21.7%
4 984
24.6%
3 671
16.8%
2 663
16.6%
1 588
14.7%
0 227
 
5.7%

Ease of Online Booking
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.765
Minimum0
Maximum5
Zeros172
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-02-21T19:49:08.695745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3945469
Coefficient of variation (CV)0.50435694
Kurtosis-0.89620936
Mean2.765
Median Absolute Deviation (MAD)1
Skewness-0.033323745
Sum11060
Variance1.9447612
MonotonicityNot monotonic
2023-02-21T19:49:08.760756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 966
24.1%
2 907
22.7%
4 759
19.0%
1 667
16.7%
5 529
13.2%
0 172
 
4.3%
ValueCountFrequency (%)
0 172
 
4.3%
1 667
16.7%
2 907
22.7%
3 966
24.1%
4 759
19.0%
5 529
13.2%
ValueCountFrequency (%)
5 529
13.2%
4 759
19.0%
3 966
24.1%
2 907
22.7%
1 667
16.7%
0 172
 
4.3%

Check-in Service
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.5 KiB
4
1130 
3
1082 
5
821 
1
497 
2
470 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row1
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 1130
28.2%
3 1082
27.1%
5 821
20.5%
1 497
12.4%
2 470
11.8%

Length

2023-02-21T19:49:08.829891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T19:49:08.912503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4 1130
28.2%
3 1082
27.1%
5 821
20.5%
1 497
12.4%
2 470
11.8%

Most occurring characters

ValueCountFrequency (%)
4 1130
28.2%
3 1082
27.1%
5 821
20.5%
1 497
12.4%
2 470
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1130
28.2%
3 1082
27.1%
5 821
20.5%
1 497
12.4%
2 470
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1130
28.2%
3 1082
27.1%
5 821
20.5%
1 497
12.4%
2 470
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1130
28.2%
3 1082
27.1%
5 821
20.5%
1 497
12.4%
2 470
11.8%

Online Boarding
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.24275
Minimum0
Maximum5
Zeros100
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-02-21T19:49:08.985209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3606194
Coefficient of variation (CV)0.41958814
Kurtosis-0.71757068
Mean3.24275
Median Absolute Deviation (MAD)1
Skewness-0.44950873
Sum12971
Variance1.8512853
MonotonicityNot monotonic
2023-02-21T19:49:09.051747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 1169
29.2%
3 827
20.7%
5 807
20.2%
2 682
17.1%
1 415
 
10.4%
0 100
 
2.5%
ValueCountFrequency (%)
0 100
 
2.5%
1 415
 
10.4%
2 682
17.1%
3 827
20.7%
4 1169
29.2%
5 807
20.2%
ValueCountFrequency (%)
5 807
20.2%
4 1169
29.2%
3 827
20.7%
2 682
17.1%
1 415
 
10.4%
0 100
 
2.5%

Gate Location
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.5 KiB
3
1105 
4
965 
2
742 
1
654 
5
534 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 1105
27.6%
4 965
24.1%
2 742
18.6%
1 654
16.4%
5 534
13.4%

Length

2023-02-21T19:49:09.121284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T19:49:09.205612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3 1105
27.6%
4 965
24.1%
2 742
18.6%
1 654
16.4%
5 534
13.4%

Most occurring characters

ValueCountFrequency (%)
3 1105
27.6%
4 965
24.1%
2 742
18.6%
1 654
16.4%
5 534
13.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1105
27.6%
4 965
24.1%
2 742
18.6%
1 654
16.4%
5 534
13.4%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1105
27.6%
4 965
24.1%
2 742
18.6%
1 654
16.4%
5 534
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1105
27.6%
4 965
24.1%
2 742
18.6%
1 654
16.4%
5 534
13.4%

On-board Service
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.5 KiB
4
1201 
5
961 
3
853 
2
543 
1
442 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row1
4th row2
5th row5

Common Values

ValueCountFrequency (%)
4 1201
30.0%
5 961
24.0%
3 853
21.3%
2 543
13.6%
1 442
 
11.1%

Length

2023-02-21T19:49:09.284692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T19:49:09.367981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4 1201
30.0%
5 961
24.0%
3 853
21.3%
2 543
13.6%
1 442
 
11.1%

Most occurring characters

ValueCountFrequency (%)
4 1201
30.0%
5 961
24.0%
3 853
21.3%
2 543
13.6%
1 442
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1201
30.0%
5 961
24.0%
3 853
21.3%
2 543
13.6%
1 442
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1201
30.0%
5 961
24.0%
3 853
21.3%
2 543
13.6%
1 442
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1201
30.0%
5 961
24.0%
3 853
21.3%
2 543
13.6%
1 442
 
11.1%

Seat Comfort
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.5 KiB
4
1187 
5
1030 
3
753 
2
576 
1
454 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row5
5th row5

Common Values

ValueCountFrequency (%)
4 1187
29.7%
5 1030
25.8%
3 753
18.8%
2 576
14.4%
1 454
 
11.3%

Length

2023-02-21T19:49:09.450175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T19:49:09.534745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4 1187
29.7%
5 1030
25.8%
3 753
18.8%
2 576
14.4%
1 454
 
11.3%

Most occurring characters

ValueCountFrequency (%)
4 1187
29.7%
5 1030
25.8%
3 753
18.8%
2 576
14.4%
1 454
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1187
29.7%
5 1030
25.8%
3 753
18.8%
2 576
14.4%
1 454
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1187
29.7%
5 1030
25.8%
3 753
18.8%
2 576
14.4%
1 454
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1187
29.7%
5 1030
25.8%
3 753
18.8%
2 576
14.4%
1 454
 
11.3%

Leg Room Service
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.36975
Minimum0
Maximum5
Zeros14
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-02-21T19:49:09.608244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.311665
Coefficient of variation (CV)0.38924697
Kurtosis-0.97771633
Mean3.36975
Median Absolute Deviation (MAD)1
Skewness-0.36131078
Sum13479
Variance1.7204651
MonotonicityNot monotonic
2023-02-21T19:49:09.673735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 1107
27.7%
5 969
24.2%
3 783
19.6%
2 730
18.2%
1 397
 
9.9%
0 14
 
0.4%
ValueCountFrequency (%)
0 14
 
0.4%
1 397
 
9.9%
2 730
18.2%
3 783
19.6%
4 1107
27.7%
5 969
24.2%
ValueCountFrequency (%)
5 969
24.2%
4 1107
27.7%
3 783
19.6%
2 730
18.2%
1 397
 
9.9%
0 14
 
0.4%

Cleanliness
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3
Minimum0
Maximum5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-02-21T19:49:09.740317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3171688
Coefficient of variation (CV)0.39914207
Kurtosis-1.0177048
Mean3.3
Median Absolute Deviation (MAD)1
Skewness-0.30919817
Sum13200
Variance1.7349337
MonotonicityNot monotonic
2023-02-21T19:49:09.804807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 1047
26.2%
3 918
22.9%
5 898
22.4%
2 633
15.8%
1 502
12.6%
0 2
 
0.1%
ValueCountFrequency (%)
0 2
 
0.1%
1 502
12.6%
2 633
15.8%
3 918
22.9%
4 1047
26.2%
5 898
22.4%
ValueCountFrequency (%)
5 898
22.4%
4 1047
26.2%
3 918
22.9%
2 633
15.8%
1 502
12.6%
0 2
 
0.1%

Food and Drink
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.20825
Minimum0
Maximum5
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-02-21T19:49:09.867805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.334289
Coefficient of variation (CV)0.41589307
Kurtosis-1.1435431
Mean3.20825
Median Absolute Deviation (MAD)1
Skewness-0.15261586
Sum12833
Variance1.780327
MonotonicityNot monotonic
2023-02-21T19:49:09.931808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 922
23.1%
5 879
22.0%
2 863
21.6%
3 848
21.2%
1 480
12.0%
0 8
 
0.2%
ValueCountFrequency (%)
0 8
 
0.2%
1 480
12.0%
2 863
21.6%
3 848
21.2%
4 922
23.1%
5 879
22.0%
ValueCountFrequency (%)
5 879
22.0%
4 922
23.1%
3 848
21.2%
2 863
21.6%
1 480
12.0%
0 8
 
0.2%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.5 KiB
4
1430 
5
1095 
3
770 
2
440 
1
265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row4
4th row2
5th row4

Common Values

ValueCountFrequency (%)
4 1430
35.8%
5 1095
27.4%
3 770
19.2%
2 440
 
11.0%
1 265
 
6.6%

Length

2023-02-21T19:49:10.005329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T19:49:10.087219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4 1430
35.8%
5 1095
27.4%
3 770
19.2%
2 440
 
11.0%
1 265
 
6.6%

Most occurring characters

ValueCountFrequency (%)
4 1430
35.8%
5 1095
27.4%
3 770
19.2%
2 440
 
11.0%
1 265
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1430
35.8%
5 1095
27.4%
3 770
19.2%
2 440
 
11.0%
1 265
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1430
35.8%
5 1095
27.4%
3 770
19.2%
2 440
 
11.0%
1 265
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1430
35.8%
5 1095
27.4%
3 770
19.2%
2 440
 
11.0%
1 265
 
6.6%

In-flight Wifi Service
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.75075
Minimum0
Maximum5
Zeros121
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-02-21T19:49:10.157822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3373749
Coefficient of variation (CV)0.48618554
Kurtosis-0.86218722
Mean2.75075
Median Absolute Deviation (MAD)1
Skewness0.013515557
Sum11003
Variance1.7885716
MonotonicityNot monotonic
2023-02-21T19:49:10.221435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 1011
25.3%
2 947
23.7%
4 773
19.3%
1 689
17.2%
5 459
11.5%
0 121
 
3.0%
ValueCountFrequency (%)
0 121
 
3.0%
1 689
17.2%
2 947
23.7%
3 1011
25.3%
4 773
19.3%
5 459
11.5%
ValueCountFrequency (%)
5 459
11.5%
4 773
19.3%
3 1011
25.3%
2 947
23.7%
1 689
17.2%
0 121
 
3.0%

In-flight Entertainment
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.368
Minimum0
Maximum5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-02-21T19:49:10.284513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.3476016
Coefficient of variation (CV)0.40011923
Kurtosis-1.0809337
Mean3.368
Median Absolute Deviation (MAD)1
Skewness-0.36751192
Sum13472
Variance1.81603
MonotonicityNot monotonic
2023-02-21T19:49:10.347977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 1093
27.3%
5 1018
25.4%
3 721
18.0%
2 681
17.0%
1 485
12.1%
0 2
 
0.1%
ValueCountFrequency (%)
0 2
 
0.1%
1 485
12.1%
2 681
17.0%
3 721
18.0%
4 1093
27.3%
5 1018
25.4%
ValueCountFrequency (%)
5 1018
25.4%
4 1093
27.3%
3 721
18.0%
2 681
17.0%
1 485
12.1%
0 2
 
0.1%

Baggage Handling
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.5 KiB
4
1417 
5
1080 
3
808 
2
408 
1
287 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row2
3rd row3
4th row2
5th row5

Common Values

ValueCountFrequency (%)
4 1417
35.4%
5 1080
27.0%
3 808
20.2%
2 408
 
10.2%
1 287
 
7.2%

Length

2023-02-21T19:49:10.418595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T19:49:10.499611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4 1417
35.4%
5 1080
27.0%
3 808
20.2%
2 408
 
10.2%
1 287
 
7.2%

Most occurring characters

ValueCountFrequency (%)
4 1417
35.4%
5 1080
27.0%
3 808
20.2%
2 408
 
10.2%
1 287
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1417
35.4%
5 1080
27.0%
3 808
20.2%
2 408
 
10.2%
1 287
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1417
35.4%
5 1080
27.0%
3 808
20.2%
2 408
 
10.2%
1 287
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1417
35.4%
5 1080
27.0%
3 808
20.2%
2 408
 
10.2%
1 287
 
7.2%

Satisfaction
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.3 KiB
Neutral or Dissatisfied
2245 
Satisfied
1755 

Length

Max length23
Median length23
Mean length16.8575
Min length9

Characters and Unicode

Total characters67430
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSatisfied
2nd rowNeutral or Dissatisfied
3rd rowNeutral or Dissatisfied
4th rowNeutral or Dissatisfied
5th rowSatisfied

Common Values

ValueCountFrequency (%)
Neutral or Dissatisfied 2245
56.1%
Satisfied 1755
43.9%

Length

2023-02-21T19:49:10.579298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T19:49:10.658505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
neutral 2245
26.4%
or 2245
26.4%
dissatisfied 2245
26.4%
satisfied 1755
20.7%

Most occurring characters

ValueCountFrequency (%)
i 10245
15.2%
s 8490
12.6%
e 6245
9.3%
t 6245
9.3%
a 6245
9.3%
r 4490
6.7%
4490
6.7%
f 4000
 
5.9%
d 4000
 
5.9%
N 2245
 
3.3%
Other values (5) 10735
15.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56695
84.1%
Uppercase Letter 6245
 
9.3%
Space Separator 4490
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 10245
18.1%
s 8490
15.0%
e 6245
11.0%
t 6245
11.0%
a 6245
11.0%
r 4490
7.9%
f 4000
 
7.1%
d 4000
 
7.1%
u 2245
 
4.0%
l 2245
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
N 2245
35.9%
D 2245
35.9%
S 1755
28.1%
Space Separator
ValueCountFrequency (%)
4490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62940
93.3%
Common 4490
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 10245
16.3%
s 8490
13.5%
e 6245
9.9%
t 6245
9.9%
a 6245
9.9%
r 4490
7.1%
f 4000
 
6.4%
d 4000
 
6.4%
N 2245
 
3.6%
u 2245
 
3.6%
Other values (4) 8490
13.5%
Common
ValueCountFrequency (%)
4490
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 10245
15.2%
s 8490
12.6%
e 6245
9.3%
t 6245
9.3%
a 6245
9.3%
r 4490
6.7%
4490
6.7%
f 4000
 
5.9%
d 4000
 
5.9%
N 2245
 
3.3%
Other values (5) 10735
15.9%

Interactions

2023-02-21T19:49:04.509422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:49.782831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:51.094225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:52.385418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:53.781218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:55.072167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:56.318926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:57.623949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:58.775859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:59.879989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:01.157529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:02.280658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:03.384048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:04.602007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:49.894277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:51.195766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:52.476998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:53.884652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:55.162510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:56.554513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:57.714520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:58.861430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:59.968020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:01.243143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:02.366446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:03.471054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:04.704537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:50.001826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:51.299717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:52.672374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:53.995249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:55.251616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:56.652033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:57.803084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:58.951954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:00.057569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:01.346726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:02.453012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:03.560570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:04.798094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-02-21T19:48:55.338850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:56.739813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:57.890631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:59.033993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:00.141154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:01.439260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:02.537583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:03.645103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:04.895164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:50.218186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:51.497729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:52.847303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:54.195225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:55.431381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:56.828343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:57.985158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:59.118582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:00.227112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:01.526841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:02.622200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:03.733631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:04.991490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:50.328727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:51.598265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:52.946855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:54.297547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:55.520917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:56.917307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:58.086694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:59.203172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:00.312718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:01.612436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:02.708771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:03.822179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:05.080530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:50.438265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:51.690996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:53.046418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:54.402076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:55.610998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:57.004871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:58.173715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:59.294735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:00.401290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:01.696082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:02.799373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:03.909704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:05.173063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:50.532815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:51.780878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:53.147969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:54.519563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:55.699127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:57.094395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:58.259271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:59.381315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:00.488882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:01.781285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:02.883479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:03.999222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:05.260616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:50.625345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:51.872416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:53.252523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:54.608160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:55.786694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:57.183791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:58.343800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:59.465315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:00.573913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:01.867328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:02.966130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:04.086749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:05.345839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:50.716778image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:52.009677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:53.351348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:54.695688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:55.874722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:57.269808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:58.435360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:59.549889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:00.815285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:01.950899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:03.049744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:04.170755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:05.429443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:50.807351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:52.109211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:53.453828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:54.791219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:56.000255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:57.354754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:58.520621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:59.632525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:00.901881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:02.032492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:03.131935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:04.255269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:05.514041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:50.894957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:52.203744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:53.552362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:54.890612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:56.097815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:57.439302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:58.604230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:59.713148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:00.988485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:02.115519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:03.215021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:04.338804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:05.604671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:50.994472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:52.296334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:53.682326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:54.979152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:56.225367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:57.530401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:58.690812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:48:59.795751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:01.072958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:02.199092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:03.300526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-21T19:49:04.426786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-02-21T19:49:10.745433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
IDAgeFlight DistanceDeparture DelayArrival DelayDeparture and Arrival Time ConvenienceEase of Online BookingOnline BoardingLeg Room ServiceCleanlinessFood and DrinkIn-flight Wifi ServiceIn-flight EntertainmentGenderCustomer TypeType of TravelClassCheck-in ServiceGate LocationOn-board ServiceSeat ComfortIn-flight ServiceBaggage HandlingSatisfaction
ID1.0000.0010.1400.054-0.0230.018-0.0070.0480.0440.0480.019-0.0460.0220.0310.0000.0000.1000.0860.0120.0430.0550.0600.0570.000
Age0.0011.0000.091-0.045-0.0270.0330.0320.2220.0700.0840.0400.0140.1000.0000.4000.3570.1950.0260.0590.0690.1180.0420.0380.271
Flight Distance0.1400.0911.0000.010-0.010-0.0080.0680.2070.1180.0700.0240.0090.0980.0130.2390.2740.3380.0540.0440.0590.0830.0430.0380.301
Departure Delay0.054-0.0450.0101.0000.745-0.022-0.024-0.054-0.007-0.023-0.019-0.028-0.0530.0130.0000.0000.0220.0000.0000.0000.0120.0390.0130.030
Arrival Delay-0.023-0.027-0.0100.7451.000-0.014-0.011-0.052-0.007-0.044-0.041-0.029-0.0700.0000.0000.0000.0000.0000.0140.0140.0110.0320.0170.017
Departure and Arrival Time Convenience0.0180.033-0.008-0.022-0.0141.0000.4240.0830.0060.0250.0180.337-0.0020.0000.3360.2980.1160.0870.4950.0520.0370.0670.0720.096
Ease of Online Booking-0.0070.0320.068-0.024-0.0110.4241.0000.3890.1130.0040.0160.7190.0550.0000.0370.1830.1200.0260.4510.0320.0390.0230.0280.333
Online Boarding0.0480.2220.207-0.054-0.0520.0830.3891.0000.1240.3280.2310.4420.2680.0380.1960.2200.2320.1400.0570.0840.2810.0830.0800.598
Leg Room Service0.0440.0700.118-0.007-0.0070.0060.1130.1241.0000.0760.0140.1510.3180.0660.0840.1740.1690.0880.0110.2740.0590.2750.2730.340
Cleanliness0.0480.0840.070-0.023-0.0440.0250.0040.3280.0761.0000.6610.0960.6670.0300.1330.0540.0980.1060.0280.0570.5700.0640.0610.297
Food and Drink0.0190.0400.024-0.019-0.0410.0180.0160.2310.0140.6611.0000.1090.6050.0190.1160.0150.0680.0540.0270.0350.5100.0420.0370.201
In-flight Wifi Service-0.0460.0140.009-0.028-0.0290.3370.7190.4420.1510.0960.1091.0000.1900.0000.0480.1820.0970.0540.3250.0920.1250.1040.1150.519
In-flight Entertainment0.0220.1000.098-0.053-0.070-0.0020.0550.2680.3180.6670.6050.1901.0000.0000.1480.1650.1530.0610.0320.3970.5380.3910.3470.418
Gender0.0310.0000.0130.0130.0000.0000.0000.0380.0660.0300.0190.0000.0001.0000.0000.0000.0000.0000.0000.0300.0360.0440.0410.000
Customer Type0.0000.4000.2390.0000.0000.3360.0370.1960.0840.1330.1160.0480.1480.0001.0000.3180.1250.0130.0950.0690.2040.0580.0460.175
Type of Travel0.0000.3570.2740.0000.0000.2980.1830.2200.1740.0540.0150.1820.1650.0000.3181.0000.5570.0000.1660.1030.0980.0600.0670.456
Class0.1000.1950.3380.0220.0000.1160.1200.2320.1690.0980.0680.0970.1530.0000.1250.5571.0000.1190.1240.1740.1560.1470.1400.506
Check-in Service0.0860.0260.0540.0000.0000.0870.0260.1400.0880.1060.0540.0540.0610.0000.0130.0000.1191.0000.0440.1340.1020.1480.1420.244
Gate Location0.0120.0590.0440.0000.0140.4950.4510.0570.0110.0280.0270.3250.0320.0000.0950.1660.1240.0441.0000.0400.0330.0420.0480.157
On-board Service0.0430.0690.0590.0000.0140.0520.0320.0840.2740.0570.0350.0920.3970.0300.0690.1030.1740.1340.0401.0000.0780.4340.4090.335
Seat Comfort0.0550.1180.0830.0120.0110.0370.0390.2810.0590.5700.5100.1250.5380.0360.2040.0980.1560.1020.0330.0781.0000.0670.0730.367
In-flight Service0.0600.0420.0430.0390.0320.0670.0230.0830.2750.0640.0420.1040.3910.0440.0580.0600.1470.1480.0420.4340.0671.0000.4830.316
Baggage Handling0.0570.0380.0380.0130.0170.0720.0280.0800.2730.0610.0370.1150.3470.0410.0460.0670.1400.1420.0480.4090.0730.4831.0000.292
Satisfaction0.0000.2710.3010.0300.0170.0960.3330.5980.3400.2970.2010.5190.4180.0000.1750.4560.5060.2440.1570.3350.3670.3160.2921.000

Missing values

2023-02-21T19:49:05.938974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-21T19:49:06.222769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDGenderAgeCustomer TypeType of TravelClassFlight DistanceDeparture DelayArrival DelayDeparture and Arrival Time ConvenienceEase of Online BookingCheck-in ServiceOnline BoardingGate LocationOn-board ServiceSeat ComfortLeg Room ServiceCleanlinessFood and DrinkIn-flight ServiceIn-flight Wifi ServiceIn-flight EntertainmentBaggage HandlingSatisfaction
5578155782Female28ReturningPersonalEconomy64600.034442313111415Satisfied
9738197382Male68ReturningPersonalEconomy102055.022122332332232Neutral or Dissatisfied
5017950180Female22First-timeBusinessEconomy109235235.013112113134333Neutral or Dissatisfied
104804104805Female56ReturningPersonalEconomy58760.052342252432222Neutral or Dissatisfied
7718277183Female32ReturningBusinessBusiness250200.000452554554055Satisfied
5030850309Female58ReturningBusinessBusiness190408.033433141421111Neutral or Dissatisfied
8670286703Male27ReturningBusinessBusiness17471010.052555452554553Satisfied
13861387Male49ReturningPersonalEconomy Plus31200.044442432334434Neutral or Dissatisfied
114549114550Male33ReturningPersonalBusiness48065.053432121222422Neutral or Dissatisfied
9115991160Female40ReturningPersonalEconomy58100.044443452444445Neutral or Dissatisfied
IDGenderAgeCustomer TypeType of TravelClassFlight DistanceDeparture DelayArrival DelayDeparture and Arrival Time ConvenienceEase of Online BookingCheck-in ServiceOnline BoardingGate LocationOn-board ServiceSeat ComfortLeg Room ServiceCleanlinessFood and DrinkIn-flight ServiceIn-flight Wifi ServiceIn-flight EntertainmentBaggage HandlingSatisfaction
9003990040Female53ReturningBusinessBusiness112710.032353545445355Satisfied
3197431975Female27First-timeBusinessBusiness10100.044342345445444Satisfied
45884589Female41ReturningBusinessEconomy41600.033413314112112Neutral or Dissatisfied
2227722278Male59ReturningBusinessEconomy20890119.022442245442445Satisfied
119887119888Male30ReturningPersonalEconomy91207.044442533445345Neutral or Dissatisfied
6001460015Female19ReturningPersonalEconomy99700.041514534335134Neutral or Dissatisfied
9302393024Female7ReturningPersonalEconomy15244846.033434423224224Neutral or Dissatisfied
104123104124Male60ReturningBusinessBusiness3433217.033553545545355Satisfied
9717197172Female24ReturningPersonalEconomy131330.034144452553354Neutral or Dissatisfied
100339100340Female36First-timeBusinessEconomy634108120.043133452554453Neutral or Dissatisfied